model 5

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 27 Dec 2010 20:39:51 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw.htm/, Retrieved Mon, 27 Dec 2010 21:37:38 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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4,24 3,353 4,15 3,186 3,93 3,902 3,7 4,164 3,7 3,499 3,65 4,145 3,55 3,796 3,43 3,711 3,47 3,949 3,58 3,74 3,67 3,243 3,72 4,407 3,8 4,814 3,76 3,908 3,63 5,25 3,48 3,937 3,41 4,004 3,43 5,56 3,5 3,922 3,62 3,759 3,58 4,138 3,52 4,634 3,45 3,996 3,36 4,308 3,27 4,143 3,21 4,429 3,19 5,219 3,16 4,929 3,12 5,761 3,06 5,592 3,01 4,163 2,98 4,962 2,97 5,208 3,02 4,755 3,07 4,491 3,18 5,732 3,29 5,731 3,43 5,04 3,61 6,102 3,74 4,904 3,87 5,369 3,88 5,578 4,09 4,619 4,19 4,731 4,2 5,011 4,29 5,299 4,37 4,146 4,47 4,625 4,61 4,736 4,65 4,219 4,69 5,116 4,82 4,205 4,86 4,121 4,87 5,103 5,01 4,3 5,03 4,578 5,13 3,809 5,18 5,657 5,21 4,248 5,26 3,83 5,25 4,736 5,2 4,839 5,16 4,411 5,19 4,57 5,39 4,104 5,58 4,801 5,76 3,953 5,89 3,828 5,98 4,44

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Lening[t] = + 5.85609938889937 -0.713759017025211Huis[t] + 0.278307172748344M1[t] + 0.00403509569409054M2[t] + 0.454093484600688M3[t] + 0.0033965967433896M4[t] + 0.0252548786472126M5[t] + 0.472496329254219M6[t] -0.208555710530405M7[t] -0.114017884567279M8[t] -0.00425688645510566M9[t] + 0.167275517466104M10[t] -0.401364442840238M11[t] + 0.0392000670189696t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 5.85609938889937 0.430642 13.5985 0 0 Huis -0.713759017025211 0.090282 -7.9059 0 0 M1 0.278307172748344 0.247509 1.1244 0.265713 0.132856 M2 0.00403509569409054 0.248528 0.0162 0.987105 0.493552 M3 0.454093484600688 0.250584 1.8121 0.075424 0.037712 M4 0.0033965967433896 0.247385 0.0137 0.989095 0.494548 M5 0.0252548786472126 0.2473 0.1021 0.919031 0.459515 M6 0.472496329254219 0.252072 1.8745 0.066183 0.033092 M7 -0.208555710530405 0.2507 -0.8319 0.409067 0.204534 M8 -0.114017884567279 0.249089 -0.4577 0.648942 0.324471 M9 -0.00425688645510566 0.247824 -0.0172 0.986358 0.493179 M10 0.167275517466104 0.259224 0.6453 0.521421 0.26071 M11 -0.401364442840238 0.262795 -1.5273 0.132419 0.06621 t 0.0392000670189696 0.002656 14.7586 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.897974932485947 R-squared 0.806358979373141 Adjusted R-squared 0.76058928358861 F-TEST (value) 17.6177482841318 F-TEST (DF numerator) 13 F-TEST (DF denominator) 55 p-value 4.21884749357559e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.408143365740899 Sum Squared Residuals 9.161955384907 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4.24 3.78037264458115 0.459627355418851 2 4.15 3.66449839038908 0.485501609610922 3 3.93 3.64270539012459 0.287294609875407 4 3.7 3.04420370682566 0.65579629317434 5 3.7 3.57991180207022 0.120088197929781 6 3.65 3.60526499469791 0.0447350053020923 7 3.55 3.21251491887405 0.337485081125947 8 3.43 3.40692232830329 0.0230776716967094 9 3.47 3.38600874738243 0.0839912526175666 10 3.58 3.74591685288088 -0.165916852880882 11 3.67 3.57121519105504 0.0987848089449601 12 3.72 3.1809642050969 0.539035794903099 13 3.8 3.20797152493495 0.592028475065046 14 3.76 3.61956518432451 0.140434815675489 15 3.63 3.15095903940224 0.479040960597756 16 3.48 3.67662780791802 -0.196627807918019 17 3.41 3.68986430270012 -0.279864302700122 18 3.43 3.06569678983487 0.364303210165131 19 3.5 3.59298208695651 -0.0929820869565105 20 3.62 3.84306269971372 -0.223062699713716 21 3.58 3.72150909739230 -0.141509097392304 22 3.52 3.57821709588798 -0.0582170958879783 23 3.45 3.50415545546269 -0.0541554554626907 24 3.36 3.72202715201003 -0.362027152010033 25 3.27 4.15730462958651 -0.887304629586506 26 3.21 3.71809754068201 -0.508097540682012 27 3.19 3.64348637315766 -0.453486373157661 28 3.16 3.43897966725664 -0.278979667256644 29 3.12 2.90619051401446 0.213809485985539 30 3.06 3.5132573055177 -0.453257305517697 31 3.01 3.89136696808107 -0.88136696808107 32 2.98 3.45481140646002 -0.474811406460021 33 2.97 3.42818775340296 -0.458187753402963 34 3.02 3.96225305905556 -0.942253059055563 35 3.07 3.62124554626285 -0.551245546262847 36 3.18 3.17603511599377 0.00396488400623347 37 3.29 3.49425611477811 -0.204256114778105 38 3.43 3.75239158550724 -0.322391585507242 39 3.61 3.48363796535203 0.126362034647966 40 3.74 3.92722444690991 -0.187224446909910 41 3.87 3.65638485291598 0.213615147084021 42 3.88 3.99365073598369 -0.113650735983685 43 4.09 4.03629366054521 0.0537063394547908 44 4.19 4.09009054362048 0.0999094563795195 45 4.2 4.03919908398457 0.160800916015435 46 4.29 4.04436895802148 0.245631041978517 47 4.37 4.33789321136418 0.0321067886358206 48 4.47 4.43656715206831 0.0334328479316890 49 4.61 4.67484714094583 -0.0648471409458255 50 4.65 4.80878854271258 -0.158788542712576 51 4.69 4.65780516036653 0.0321948396334716 52 4.82 4.89654280403817 -0.0765428040381673 53 4.86 5.01755691039108 -0.157556910391077 54 4.87 4.8030870732983 0.0669129267017037 55 5.01 4.73438359120389 0.275616408796113 56 5.03 4.66969647745297 0.360303522547026 57 5.13 5.3675382266765 -0.237538226676504 58 5.18 4.25924403415409 0.920755965845907 59 5.21 4.73549059585524 0.474509404144757 60 5.26 5.47440637483099 -0.214406374830989 61 5.25 5.14524794517346 0.104752054826539 62 5.2 4.83665875638458 0.363341243615420 63 5.16 5.63140607159694 -0.471406071596938 64 5.19 5.1064215670516 0.0835784329484001 65 5.39 5.50009161790814 -0.110091617908142 66 5.58 5.48904310066755 0.0909568993324552 67 5.76 5.45245877433927 0.307541225660729 68 5.89 5.67541654444952 0.214583455550482 69 5.98 5.38755709116123 0.592442908838769 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00491545373388572 0.00983090746777144 0.995084546266114 18 0.0047440583480091 0.0094881166960182 0.99525594165199 19 0.0056959265459691 0.0113918530919382 0.99430407345403 20 0.0240644690316944 0.0481289380633888 0.975935530968306 21 0.0254592197007167 0.0509184394014334 0.974540780299283 22 0.0243200850625037 0.0486401701250073 0.975679914937496 23 0.0210948974623953 0.0421897949247906 0.978905102537605 24 0.0482331242996776 0.0964662485993552 0.951766875700322 25 0.234226395882883 0.468452791765766 0.765773604117117 26 0.352853021618478 0.705706043236956 0.647146978381522 27 0.385221063314622 0.770442126629244 0.614778936685378 28 0.377957174507464 0.755914349014928 0.622042825492536 29 0.339332349861773 0.678664699723546 0.660667650138227 30 0.268232339658710 0.536464679317421 0.73176766034129 31 0.210222810665975 0.420445621331949 0.789777189334026 32 0.178091631555992 0.356183263111983 0.821908368444008 33 0.178015215750953 0.356030431501907 0.821984784249047 34 0.308981903345176 0.617963806690352 0.691018096654824 35 0.494601699520537 0.989203399041074 0.505398300479463 36 0.516224340675351 0.967551318649297 0.483775659324649 37 0.648734327802596 0.702531344394809 0.351265672197404 38 0.820709258270293 0.358581483459414 0.179290741729707 39 0.930382649842197 0.139234700315606 0.0696173501578028 40 0.982562052057468 0.0348758958850641 0.0174379479425321 41 0.9943046541866 0.0113906916268002 0.0056953458134001 42 0.996757726385094 0.0064845472298114 0.0032422736149057 43 0.998282293824407 0.00343541235118524 0.00171770617559262 44 0.998667146056445 0.00266570788710969 0.00133285394355484 45 0.999224777817489 0.00155044436502294 0.000775222182511468 46 0.999418486148183 0.00116302770363367 0.000581513851816834 47 0.999148842747714 0.00170231450457125 0.000851157252285625 48 0.99767342646437 0.00465314707125798 0.00232657353562899 49 0.992827967391555 0.0143440652168904 0.0071720326084452 50 0.97944403044171 0.041111939116581 0.0205559695582905 51 0.96712016808991 0.065759663820181 0.0328798319100905 52 0.969222676427189 0.0615546471456222 0.0307773235728111 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.25 NOK 5% type I error level 17 0.472222222222222 NOK 10% type I error level 21 0.583333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/10e5yy1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/10e5yy1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/175251293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/175251293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/275251293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/275251293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/3ie1q1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/3ie1q1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/4ie1q1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/4ie1q1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/5ie1q1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/5ie1q1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/6t5ia1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/6t5ia1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/7t5ia1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/7t5ia1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/8lezv1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/8lezv1293482382.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/9lezv1293482382.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293482258ltujx9c259wh1kw/9lezv1293482382.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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